A machine vision camera consists of an image sensor, a processor and a neural processing unit, this makes it possible to perform edge computing on the captured image. Performing inference near the data source reduces the load to transfer and process images on the centralized server. This helps in reducing the latency in processing the data and improves reliability of the overall system. Recently, deep neural networks had great improvements in solving image classification problems. This was achieved by the advent of convolutional neural networks (CNNs). Successful CNN approaches are able to solve image classification problems with an accuracy near to human [1]}. However, CNNs need considerable resources of computation and memory.
The idea of executing a deep learning algorithm into an embedded device has been discussed widely and solutions such as compression are provided [2]}, [3]}. In this paper, we formulate the grasp verification problem as deep learning based image classification task. However, the two main challenges with performing deep learning inference in machine vision cameras are (i) lack of literature on the performance of these cameras and (ii) establishing a deep learning architecture that is capable to fulfill the visual classification task with satisfying accuracy reckoning the limitations of the embedded device.
